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Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders
OctaMem · Persistent memory for AI systems
Mossiah · 2026-06-22 · via Hacker News - Newest: "AI"

§ 01Problem & solution

The cost of forgetting.

Agents without memory fail in two ways that show up on the balance sheet: they burn tokens re-reading context, and they let hard-won institutional knowledge walk out the door. A memory layer answers both.

01The cost

Token spend compounds every turn.

Without memory, the same context is re-sent on every call. Conversations re-explain themselves, prompts balloon, and you pay frontier-model rates to re-read what the model was already told an hour ago.

The solution

Send less. Repeat nothing. Pay less.

  • Less context per call — only what's relevant is retrieved and injected
  • No repetition — facts and decisions persist instead of being re-sent
  • Cheaper models hold their own once the context they receive is sharper

02The leak

Institutional knowledge isn't centralised.

What your agents and teams learn lives in scattered sessions, local notes, and individual heads. When an employee leaves, it leaves with them. Nothing compounds, and nothing is owned by the organisation.

The solution

One memory the whole organisation owns.

  • Organisation-wide intelligence — every agent reads from one shared layer
  • Knowledge stays when people leave — it lives in the memory, not the person
  • Context compounds across teams instead of resetting every session

Memory has to be infrastructure — not a patch.

See how the architecture solves it

§ 02The Architecture

Memory in motion.

Every request passes through the same disciplined cycle. OctaMem doesn’t fire a generic search across one bucket of text. It rebuilds context from three memory types that each serve a distinct purpose, then reassembles them for the model.

Read the technical brief

semanticepisodicprocedural01Appor MCPCaller02SecuritylayerAccess · quota03RetrievalserviceOctaMem agent04MemorylayersThree layers05UnifiedcontextBack to app

fig. 1 · Search cycle · stage 1 of 5

fig. 1 · Search · stage 1 / 5

§ 03File ingestion

Any file. Now memory.

Hand OctaMem the document itself. Contracts, decks, spreadsheets, emails, PDFs. We parse, structure, and store it as typed memory your agents can query forever.

Not embeddings of a blob. Clauses, parties, obligations.

Batch upload
5 files

Avg pages
40

Max file
30 MB

Retention
Configurable

Drop the file. Memory does the rest.

contract-v3.pdfPDF

Master Services Agreementparties · term · obligations

Parsed memory record

contract-v3.pdf

Master Services Agreement,
v3 · executed 2026-04-12

  • parties: Acme Corp, OctaMem Inc.
  • term: 24 months, auto-renew 12
  • obligations: 99.9% uptime SLA, 30-day deletion

Searchable across the account under previous_context: legal-msas.

§ From input to inheritance

§ 04Compounding intelligence

Intelligence that compounds.

Every session without memory is a reset. Every session with memory is an upgrade.

Day 1

Recognition

Day 30

Pattern awareness

Day 180

Operational depth

fig. 2 · capability over time. Day 360 is off this chart, the curve keeps climbing.
  1. 0Day 1

    Recognition.

    Names, preferences, initial constraints. Conversations feel slightly personalized. The kind a thoughtful intern manages on day one.

  2. 0Day 30

    Pattern awareness.

    The agent remembers your decisions, avoids past mistakes, and follows your workflows without repeated instruction. Fewer questions, fewer corrections.

  3. 0Day 180

    Operational depth.

    Deep institutional context. The agent operates with continuity across teams, releases, and tools. A system of record your AI can actually use.

Day 360 isn’t on the chart. The curve keeps climbing.

+ Compounds with every session

One memory layer. Two paths.

Start on the general cloud, or run on a vertical-specific memory cloud tuned to your sector’s schemas, policies, and compliance posture.

SEARCH PATHreadsONE MEMORY LAYERSsemanticfacts & knowledgeEepisodicevents & historyPproceduralworkflows & ruleswritesADD PATH
fig. 3 · search reads, add writes, same three layers.

§ 06Use cases

Built for real systems.

The same memory layer, accessed however your team already builds. No bespoke vertical stack. No rewrite. The platform shapes to the workflow, not the other way around.

Coverage at a glance

Verticals

08

Healthcare, finance, defense, public sector.

Runtimes

08

REST, MCP, SDKs, IDE plugins.

Memory layer

01

Unified across stacks.

Stack rewrites

00

Drop in through existing interfaces.

i.

Enterprise verticals

§ 07For the enterprise

Built for the high-stakes stack.

When memory integrity matters, when decisions need traceability, when continuity is not optional. OctaMem is the layer your security, compliance, and infrastructure teams will actually approve.

Trust & securityTalk to enterprise

§ 08In practice

Same memory. Five runtimes.

The full integration. No vector DB to operate. No embedding pipeline to maintain. No chunking. OctaMem holds the memory; you keep your stack — Python, JavaScript, REST, or MCP.

  • add(). Capture a memory with its previous context.
  • get() / search(). Recall it from any agent, any session.
  • MCP. Same operations as tool-calls in any MCP-compatible client.

Read the docsSDK reference

quickstart.py · python

from octamem import OctaMem

# Your API key from platform.octamem.com.
client = OctaMem(api_key="sk-om-live-...")

# Capture a memory.
client.add(
    content="Beta opens March 20.",
    previous_context="Q1 product launch",
)

# Recall it later, possibly from a different agent.
results = client.get(
    query="When does beta open?",
    previous_context="Q1 product launch",
)
print(results)

response · memory.search()200 · application/json

{
  "results": [
    { "id": "rec_01HV4Z…", "type": "semantic", "score": 0.94,
      "content": "Beta opens March 20.",
      "source": "planning_doc_q1", "created_at": "2026-02-14T09:12Z" },
    { "id": "rec_01HV7M…", "type": "episodic", "score": 0.88,
      "content": "Approved Q1 scope reduction on 2026-02-09." },
    { "id": "rec_01HV9F…", "type": "procedural", "score": 0.81 }
  ],
  "tokens": 642, "previous_context": "Q1 product launch"
}

Source-linked. Every record carries id, type, score, content, and source — auditable end-to-end, deletable by id or by previous_context.

§ 09Trust & control

Your memory.
Your control.

Memory is sensitive. See what is stored, keep it structured and traceable, and delete it whenever you want. No opaque embeddings. No locked-in vendor format.

Audit chain

Every memory action leaves a mark.

Reads, writes, redactions, and policy checks are chained together so the record can be inspected after the fact.


Active event

context.delivered

hash: sha256:ad72f9019c

  • EVT_4182prev: 0b91ce774a

    recall.requested

    agent:legal-copilotacme/legal/msas

  • EVT_4183prev: 8f4a2c91b0

    policy.checked

    policy:contract-scoperedact: pricing / pii

  • EVT_4184prev: 1c68bd044e

    context.delivered

    octamem:renderer642 tokens / 7 sources

  • EVT_4185prev: ad72f9019c

    memory.captured

    agent:legal-copilotretention: 365 days

Compliance posture

4 frameworks

Infrastructure

  • Encryption

    AES-256-GCM at rest · TLS 1.3 in transit · BYO-KMS on Enterprise

  • Access control

    Role-based scopes · SSO via Okta, Entra, Google · SCIM

  • Audit logging

    Immutable append-only log · per-record provenance

  • Retention

    Configurable windows · scoped delete by record or context

  • Observability

    Per-tenant metrics, latency, error budgets · Datadog export

  • Resilience

    Multi-AZ · RTO 30m / RPO 5m

Deployment

Read the full reportData processing addendumSubprocessors

§ 10Frequently asked

The questions
we always get.

Six of the most common things buyers ask in the first conversation. If yours isn’t here, send us a noteand we’ll add it.

Desktop app

Memory, native on your Mac.

The OctaMem desktop app brings capture and recall to macOS without a browser tab. Same account, same memory layer, one keystroke away while you work.

Apple Silicon and Intel · Windows and Linux coming

fig. 5 · native recall, no browser tab.

Get the desktop app

Stop resetting.
Start remembering.

Persistent memory infrastructure for every agent, every model, every workflow your organization runs. With the audit trail your security team requires and the simplicity your developers expect.

No card required · Free tier includes 2 GB memory